Basic genetic map construction
Min-Yao Jhu
2021-03-03
Learning Goals
At the end of this exercise, you will be able to: 1. know the concept of F2 intercross and the standard input format for R/qtl 2. summarize and understand the F2 intercross data 3. construct basic genetic map using R/qtl 4. create interactive graphics for genetic mapping using R/qtlcharts
Resources and references
F2 intercross strategy
Load the libraries
library(qtl)
library(qtlcharts)
knitr::opts_chunk$set(fig.width=8, fig.height=6, message=FALSE)The function read.cross is for importing data into R/qtl.
#?read.crossExample 1: Identify genomic regions that regulate blood pressure, heart rate, and heart weight
We will consider data from Sugiyama et al., Physiol Genomics 10:5–12, 2002. The data are from an intercross between BALB/cJ and CBA/CaJ; only male offspring were considered. There are four phenotypes: blood pressure, heart rate, body weight, and heart weight. “sug.csv” is the name of the file, which we import directly from the R/qtl website. genotypes indicates the codes used for the genotypes; alleles indicates single-character codes to be used in plots and such.
sug <- read.cross("csv", "https://rqtl.org", "sug.csv",
genotypes=c("CC", "CB", "BB"), alleles=c("C", "B")) #Specifying genotype you have## --Read the following data:
## 163 individuals
## 93 markers
## 6 phenotypes
## --Cross type: f2Summaries
Get a quick summary of the data.
summary(sug)## F2 intercross
##
## No. individuals: 163
##
## No. phenotypes: 6
## Percent phenotyped: 95.1 95.7 99.4 99.4 100 100
##
## No. chromosomes: 19
## Autosomes: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
##
## Total markers: 93
## No. markers: 5 7 5 5 5 4 8 4 4 5 6 3 3 5 5 4 4 6 5
## Percent genotyped: 98.3
## Genotypes (%): CC:23.9 CB:50.2 BB:26.0 not BB:0.0 not CC:0.0head(sug$pheno)## bp hr bw heart_wt sex mouse_ID
## 1 104 517 37.0 133 1 3
## 2 108 690 38.9 135 1 4
## 3 115 653 43.8 159 1 5
## 4 119 592 42.5 131 1 6
## 5 NA NA 43.6 128 1 8
## 6 106 517 35.3 175 1 9#Also specifies ID of mouse. so counts as phenotype
#Have sex, but all mice are male, hence why it's 1
#Quantitative traits: bp, hr, bw, heart_wtThere are a number of simple functions for pulling out pieces of summary information.
nind(sug) # No. individuals## [1] 163nphe(sug) # No. phenotypes## [1] 6nchr(sug) # No. chromosomes## [1] 19totmar(sug) # Total markers## [1] 93nmar(sug) # No. markers on each chromosome## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
## 5 7 5 5 5 4 8 4 4 5 6 3 3 5 5 4 4 6 5Get a summary plot of the data.
plot(sug)
Individual parts of the above plot may be obtained as follows.
#?plotMissing
#Indicates it finds the missing data on the plot
plotMissing(sug, main="")
plotMissing(sug, main="", reorder=1)
#?plotMap
plotMap(sug)
plotMap(sug,show.marker.names=TRUE)
#?plotPheno
plotPheno(sug, pheno.col=1)
plotPheno(sug, pheno.col=2)
plotPheno(sug, pheno.col=3)
plotPheno(sug, pheno.col=4)
Let’s make an interactive chart of the genetic map of markers for these data using R/qtlcharts.
iplotMap(sug) #D7MIT31Let’s use iplotCorr to plot a heat map of the correlation matrix for the phenotype of all pairs, linked to scatterplots of the phenotypes.
iplotCorr(sug$pheno)## Warning in stats::cor(mat, use = "pairwise.complete.obs"): the standard
## deviation is zeroiplotCorr(sug$pheno, chartOpts=list(height=300, width=600, scatcolors="pink"))## Warning in stats::cor(mat, use = "pairwise.complete.obs"): the standard
## deviation is zeroThe interactive graphs produced by R/qtlcharts are, by default, saved to a temporary file and then opened in the default web browser. If you want to save a chart to a particular file, assign the result to some object and use the function saveWidget() in the [htmlwidgets package] (http://www.htmlwidgets.org/), as follows:
corrplot <- iplotCorr(sug$pheno, chartOpts=list(height=300, width=600, scatcolors="pink"))## Warning in stats::cor(mat, use = "pairwise.complete.obs"): the standard
## deviation is zerohtmlwidgets::saveWidget(corrplot, file="iplotCorr_example.html")Quantitative Trait Locus (QTL) Analysis
Quantitative trait locus (QTL) analysis is a statistical method that links two types of information—phenotypic data (trait measurements) and genotypic data (usually molecular markers)—in an attempt to explain the genetic basis of variation in complex traits (Falconer & Mackay, 1996; Kearsey, 1998; Lynch & Walsh, 1998). Quantitative Trait Locus (QTL) Analysis
LOD Score
LOD score is actually an acronym for “log of the odds,” LOD. LOD score actually refers to a numerical result when estimating whether two genes, or a gene and a disease, are linked to one another. NIH LOD Score
Single-QTL analysis
We first calculate the QTL genotype probabilities, given the observed marker data, via the function calc.genoprob. This is done at the markers and at a grid along the chromosomes. The argument step is the density of the grid (in cM), and defines the density of later QTL analyses. To perform a single-QTL genome scan, we use the function scanone. By default, it considers the first phenotype in the input cross object (in this case, blood pressure).
sug <- calc.genoprob(sug, step=1) #Indicates density used for calculation, or centimorgan
out.em <- scanone(sug)## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 8 individuals with missing phenotypes.out.em2 <- scanone(sug, pheno.col=1:4)## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 8 individuals with missing phenotypes.## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 7 individuals with missing phenotypes.## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 1 individuals with missing phenotypes.
## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 1 individuals with missing phenotypes.#Dropping phenotypes, so when assessing differences, it will use the first phenotype (ex. in this case blood pressure)The output has “class” “scanone”. The summary function is passed to the function summary.scanone, and gives the maximum LOD score on each chromosome.
summary(out.em)## chr pos lod
## D1MIT36 1 76.73 1.449
## c2.loc77 2 82.80 1.901
## c3.loc42 3 52.82 1.393
## c4.loc43 4 47.23 0.795
## D5MIT223 5 86.57 1.312
## c6.loc26 6 27.81 0.638
## c7.loc45 7 47.71 6.109
## c8.loc34 8 54.90 1.598
## D9MIT71 9 27.07 0.769
## c10.loc51 10 60.75 0.959
## c11.loc34 11 38.70 2.157
## D12MIT145 12 2.23 1.472
## c13.loc20 13 27.26 1.119
## D14MIT138 14 12.52 1.119
## c15.loc8 15 11.96 5.257
## c16.loc31 16 45.69 0.647
## D17MIT16 17 17.98 1.241
## D18MIT22 18 13.41 1.739
## D19MIT71 19 56.28 0.402summary(out.em2)## chr pos bp hr bw heart_wt
## D1MIT36 1 76.73 1.449 0.9306 0.8418 1.10701
## c2.loc77 2 82.80 1.901 3.2352 0.7491 0.14570
## c3.loc42 3 52.82 1.393 0.0199 1.7099 0.22918
## c4.loc43 4 47.23 0.795 0.9465 0.5097 0.40329
## D5MIT223 5 86.57 1.312 0.1639 1.1748 0.05581
## c6.loc26 6 27.81 0.638 0.0762 0.7033 0.15187
## c7.loc45 7 47.71 6.109 0.2060 0.5209 0.47587
## c8.loc34 8 54.90 1.598 0.3938 0.1101 1.50973
## D9MIT71 9 27.07 0.769 0.4386 0.0748 0.07482
## c10.loc51 10 60.75 0.959 0.9614 0.3983 2.60545
## c11.loc34 11 38.70 2.157 1.1985 0.5037 0.00473
## D12MIT145 12 2.23 1.472 0.3669 1.1148 0.66280
## c13.loc20 13 27.26 1.119 0.5487 0.0562 0.29918
## D14MIT138 14 12.52 1.119 0.0612 0.1672 1.39513
## c15.loc8 15 11.96 5.257 1.6771 5.6325 1.26297
## c16.loc31 16 45.69 0.647 0.4475 2.3418 0.07920
## D17MIT16 17 17.98 1.241 1.1660 0.0712 0.74646
## D18MIT22 18 13.41 1.739 0.9585 0.3425 1.39105
## D19MIT71 19 56.28 0.402 0.3015 0.0136 0.25134Alternatively, we can give a threshold, e.g., to only see those chromosomes with LOD > 3.
summary(out.em, threshold=3)## chr pos lod
## c7.loc45 7 47.7 6.11
## c15.loc8 15 12.0 5.26We can plot the results as follows.
plot(out.em)
plot(out.em, chr=c(7,15), ylab="LOD Score")
### iplotScanone iplotScanone creates an interactive chart with LOD curves from a genome scan linked to estimated QTL effects. If you provide just the output from scanone, the only interactivity is that hovering over marker positions on the LOD curves will give information about the marker name, position, and LOD score.
iplotScanone(out.em)You can use the chr to plot only selected chromosomes.
iplotScanone(out.em, chr=c(7,15))iplotScanone(out.em, sug)#Lets you click on specific phenotypeiplotScanone(out.em, sug, chr=c(7,15)) #Limiting scan of to chromosome 7 and 15Example 2: Gravitropism in Arabidopsis
We’ll consider the grav dataset included with R/qtlcharts. These are data from Moore et al. Genetics 195:1077-1086, 2013, on a QTL experiment on gravitropism in Arabidopsis, with data on 162 recombinant inbred lines (Ler × Cvi). Seedlings were sprouted and then rotated 90 degrees with respect to gravity; the growth of the seedlings was then recorded on video. The outcome is the root tip angle (in degrees) at two-minute increments over eight hours.
data(grav)Practice
- Get a quick summary of the grav data.
summary(grav)## RI strains via selfing
##
## No. individuals: 162
##
## No. phenotypes: 241
## Percent phenotyped: 100
##
## No. chromosomes: 5
## Autosomes: 1 2 3 4 5
##
## Total markers: 234
## No. markers: 26 42 64 35 67
## Percent genotyped: 98.6
## Genotypes (%): LL:56.5 CC:43.5- Make an genetic map of markers for the grav data.
plotMap(grav)
iplotMap(grav)- Plot a heat map of the correlation matrix for the phenotype at all pairs of times, linked to scatterplots of the phenotypes.
head(grav$pheno)## T0 T2 T4 T6 T8 T10 T12 T14
## 1 -3.4717 -3.2973 -3.4837 -3.8324 -3.9971 -4.4138 -4.6477 -4.9400
## 2 -3.9243 -4.1068 -4.0559 -4.1955 -4.2935 -4.5356 -4.9223 -5.2457
## 3 -7.4022 -7.4470 -7.6809 -7.7515 -8.2363 -8.7546 -9.1621 -9.9663
## 4 -11.0550 -11.0470 -11.1190 -10.8650 -10.9140 -11.2740 -11.2450 -11.2770
## 5 -5.0697 -5.1864 -5.0430 -5.4597 -5.8597 -6.3028 -7.1255 -7.4619
## 6 5.1926 5.3676 5.1018 4.8896 3.9455 3.4340 2.5305 1.8841
## T16 T18 T20 T22 T24 T26 T28 T30
## 1 -5.4629 -5.89310 -6.5397 -6.9486 -7.7236 -8.3297 -8.9201 -9.6447
## 2 -5.5971 -5.89990 -6.1434 -6.6733 -6.9014 -7.5466 -8.0077 -8.3959
## 3 -10.2640 -10.84700 -11.8350 -12.3950 -13.1180 -13.7720 -14.6950 -15.5160
## 4 -11.2500 -11.58300 -11.6780 -11.7850 -12.1060 -12.3240 -12.7620 -13.1980
## 5 -8.3286 -8.80330 -9.7265 -10.7910 -11.7790 -12.3320 -13.4920 -14.5850
## 6 1.1394 0.12565 -0.7945 -1.5589 -2.3704 -3.5434 -4.5057 -5.5834
## T32 T34 T36 T38 T40 T42 T44 T46 T48
## 1 -10.5440 -11.3190 -12.2730 -12.8690 -13.684 -14.672 -15.574 -16.025 -17.142
## 2 -8.7834 -9.3773 -9.8818 -10.5020 -11.141 -11.801 -12.305 -12.818 -13.592
## 3 -16.4600 -17.3470 -17.9860 -19.0310 -20.076 -20.788 -21.927 -22.833 -23.803
## 4 -13.1540 -13.4740 -14.0890 -14.4260 -14.644 -14.873 -15.340 -15.668 -15.841
## 5 -15.6780 -16.7270 -17.9550 -19.1010 -20.226 -21.375 -22.675 -23.858 -25.091
## 6 -6.6111 -7.7473 -8.7888 -9.7522 -10.943 -12.453 -13.494 -14.338 -15.433
## T50 T52 T54 T56 T58 T60 T62 T64 T66
## 1 -17.971 -18.896 -19.540 -20.474 -21.435 -22.227 -23.179 -23.962 -24.929
## 2 -14.161 -15.022 -15.342 -16.059 -16.675 -17.457 -18.453 -19.111 -19.915
## 3 -24.647 -25.486 -26.355 -27.266 -28.387 -29.124 -29.933 -30.812 -31.520
## 4 -16.482 -16.764 -17.297 -17.689 -17.731 -18.308 -18.985 -19.643 -20.051
## 5 -26.122 -27.322 -28.556 -29.358 -30.675 -31.585 -32.698 -33.580 -34.640
## 6 -16.654 -17.801 -18.894 -19.951 -21.298 -22.422 -23.611 -24.827 -26.050
## T68 T70 T72 T74 T76 T78 T80 T82 T84
## 1 -25.849 -26.512 -27.360 -28.071 -28.876 -29.480 -30.484 -31.342 -32.066
## 2 -20.607 -21.012 -21.775 -22.677 -23.546 -24.047 -25.123 -25.930 -26.861
## 3 -32.607 -33.320 -34.355 -35.021 -35.973 -36.892 -37.663 -38.233 -39.279
## 4 -20.590 -20.826 -21.377 -22.112 -22.785 -23.291 -23.720 -24.493 -24.903
## 5 -35.493 -36.559 -37.400 -38.325 -39.268 -40.199 -40.940 -42.132 -43.133
## 6 -27.223 -28.538 -29.651 -30.447 -31.601 -32.785 -34.192 -34.898 -36.201
## T86 T88 T90 T92 T94 T96 T98 T100 T102
## 1 -32.975 -33.658 -34.361 -35.217 -35.801 -36.682 -37.191 -37.905 -38.623
## 2 -27.574 -28.510 -29.406 -30.092 -30.902 -31.909 -32.502 -33.525 -33.981
## 3 -40.028 -40.864 -41.413 -42.298 -42.773 -43.850 -44.697 -45.225 -45.846
## 4 -25.590 -26.123 -26.627 -27.198 -27.916 -28.560 -29.095 -29.969 -30.284
## 5 -44.022 -44.890 -45.764 -47.025 -47.880 -48.632 -49.481 -50.147 -51.120
## 6 -37.196 -38.119 -39.241 -40.157 -40.958 -41.737 -42.447 -43.291 -44.126
## T104 T106 T108 T110 T112 T114 T116 T118 T120
## 1 -39.276 -39.972 -40.838 -41.169 -41.709 -42.570 -43.236 -43.844 -44.508
## 2 -34.874 -35.465 -36.392 -37.208 -37.845 -38.638 -39.458 -40.161 -40.790
## 3 -46.420 -47.342 -47.946 -48.515 -49.104 -49.841 -50.631 -51.493 -51.674
## 4 -30.875 -31.379 -31.936 -32.860 -33.621 -34.162 -34.754 -35.579 -36.356
## 5 -52.096 -52.939 -53.586 -54.542 -55.245 -56.242 -56.697 -57.401 -58.114
## 6 -44.981 -45.666 -46.292 -46.933 -47.495 -48.041 -48.875 -49.195 -49.590
## T122 T124 T126 T128 T130 T132 T134 T136 T138
## 1 -45.052 -45.742 -46.536 -47.143 -47.731 -48.357 -48.989 -49.582 -50.113
## 2 -41.386 -41.937 -42.846 -43.601 -44.303 -44.793 -45.647 -46.480 -46.976
## 3 -52.500 -53.008 -53.776 -54.210 -55.042 -55.609 -56.057 -56.817 -57.477
## 4 -36.638 -37.451 -38.116 -38.886 -39.710 -40.586 -40.961 -41.802 -42.623
## 5 -58.944 -59.784 -60.385 -60.950 -61.756 -62.497 -63.080 -63.700 -64.356
## 6 -50.020 -51.037 -51.472 -51.771 -52.637 -53.307 -53.614 -54.106 -55.052
## T140 T142 T144 T146 T148 T150 T152 T154 T156
## 1 -50.738 -51.421 -52.148 -52.767 -53.312 -53.971 -54.504 -54.942 -55.626
## 2 -47.754 -48.466 -49.081 -49.700 -50.411 -51.143 -51.633 -52.219 -52.914
## 3 -58.019 -58.512 -59.319 -60.087 -60.592 -61.258 -62.023 -62.501 -63.018
## 4 -43.263 -44.073 -44.705 -45.405 -46.227 -46.777 -47.665 -48.273 -48.942
## 5 -65.014 -65.455 -66.229 -66.747 -67.478 -67.972 -68.777 -69.356 -69.908
## 6 -55.645 -56.311 -56.876 -57.440 -57.880 -58.532 -59.336 -59.592 -59.760
## T158 T160 T162 T164 T166 T168 T170 T172 T174
## 1 -56.166 -56.842 -57.229 -57.744 -58.272 -58.910 -59.321 -59.720 -60.315
## 2 -53.578 -54.335 -54.647 -55.308 -56.069 -56.775 -57.100 -57.883 -58.678
## 3 -63.808 -64.185 -64.809 -65.401 -65.899 -66.322 -66.832 -67.566 -68.177
## 4 -49.860 -50.554 -51.010 -52.018 -52.724 -53.145 -53.799 -54.451 -55.146
## 5 -70.409 -71.122 -71.708 -72.204 -72.922 -73.402 -73.978 -74.665 -75.099
## 6 -60.479 -61.198 -61.477 -62.208 -62.526 -63.198 -63.795 -64.299 -64.718
## T176 T178 T180 T182 T184 T186 T188 T190 T192
## 1 -60.667 -61.412 -61.784 -62.301 -62.698 -63.181 -63.630 -64.243 -64.534
## 2 -59.000 -59.640 -60.296 -60.896 -61.238 -61.848 -62.529 -62.856 -63.434
## 3 -68.624 -69.035 -69.486 -69.937 -70.426 -70.882 -71.364 -71.729 -72.124
## 4 -56.160 -56.701 -57.650 -58.093 -58.820 -59.786 -59.997 -60.955 -61.331
## 5 -75.632 -76.167 -76.740 -77.351 -77.759 -78.294 -78.932 -79.457 -79.998
## 6 -65.145 -65.779 -66.081 -66.778 -67.294 -67.950 -68.451 -68.720 -68.932
## T194 T196 T198 T200 T202 T204 T206 T208 T210
## 1 -64.890 -65.510 -66.168 -66.566 -66.998 -67.456 -67.958 -68.519 -69.025
## 2 -63.972 -64.391 -64.868 -65.522 -65.781 -66.487 -66.938 -67.413 -67.899
## 3 -72.640 -73.153 -73.494 -73.927 -74.286 -74.804 -75.422 -75.865 -75.938
## 4 -62.151 -62.819 -63.376 -63.922 -64.867 -65.260 -65.847 -66.765 -67.227
## 5 -80.397 -80.876 -81.357 -81.888 -82.368 -82.940 -83.207 -83.678 -84.140
## 6 -69.186 -69.873 -70.079 -70.873 -71.243 -71.757 -72.046 -72.763 -73.357
## T212 T214 T216 T218 T220 T222 T224 T226 T228
## 1 -69.525 -69.888 -70.340 -70.804 -71.071 -71.628 -71.982 -72.534 -72.884
## 2 -68.506 -68.917 -69.466 -69.803 -70.341 -70.645 -71.224 -71.558 -72.071
## 3 -76.868 -77.163 -77.641 -78.014 -78.117 -78.610 -78.825 -79.463 -79.601
## 4 -67.731 -68.571 -69.307 -69.675 -70.307 -70.896 -71.366 -72.210 -72.740
## 5 -84.789 -85.156 -85.362 -85.674 -86.207 -86.811 -87.143 -87.494 -87.886
## 6 -74.028 -74.683 -75.128 -75.417 -75.609 -76.169 -76.466 -76.502 -76.959
## T230 T232 T234 T236 T238 T240 T242 T244 T246
## 1 -73.329 -73.717 -74.223 -74.507 -74.996 -75.494 -75.904 -76.131 -76.718
## 2 -72.568 -73.052 -73.485 -74.223 -74.447 -74.597 -75.212 -75.660 -76.156
## 3 -79.812 -80.096 -80.464 -80.789 -80.941 -81.391 -81.544 -81.752 -82.099
## 4 -73.236 -73.972 -74.239 -74.816 -75.458 -76.175 -76.523 -76.996 -77.571
## 5 -88.418 -88.701 -89.255 -89.735 -90.126 -90.510 -90.915 -91.455 -91.664
## 6 -77.377 -77.588 -78.068 -77.785 -78.312 -78.332 -78.877 -79.312 -79.207
## T248 T250 T252 T254 T256 T258 T260 T262 T264
## 1 -76.914 -77.289 -77.691 -77.903 -78.405 -78.835 -79.148 -79.571 -79.897
## 2 -76.597 -76.938 -77.248 -77.659 -77.926 -78.193 -78.633 -79.153 -79.166
## 3 -82.477 -82.717 -82.892 -83.298 -83.495 -83.671 -83.969 -84.261 -84.679
## 4 -77.896 -78.290 -78.925 -79.255 -79.696 -80.135 -80.729 -81.152 -81.865
## 5 -91.969 -92.461 -92.869 -93.271 -93.527 -93.960 -94.234 -94.495 -94.789
## 6 -79.655 -79.957 -80.278 -81.010 -81.135 -81.441 -81.601 -81.921 -82.378
## T266 T268 T270 T272 T274 T276 T278 T280 T282
## 1 -80.146 -80.465 -80.836 -81.189 -81.380 -81.813 -82.299 -82.509 -82.760
## 2 -79.639 -79.781 -80.089 -80.429 -80.590 -80.940 -81.026 -81.324 -81.550
## 3 -84.798 -85.107 -85.661 -85.590 -85.700 -86.107 -86.165 -86.559 -86.784
## 4 -82.150 -82.662 -82.905 -83.314 -83.757 -84.402 -84.452 -85.065 -85.416
## 5 -94.885 -95.173 -95.431 -95.605 -95.727 -95.988 -96.357 -96.489 -96.921
## 6 -82.489 -83.009 -83.161 -83.831 -84.098 -84.380 -84.777 -84.856 -85.414
## T284 T286 T288 T290 T292 T294 T296 T298 T300
## 1 -83.180 -83.445 -83.913 -84.118 -84.649 -85.063 -85.222 -85.364 -85.635
## 2 -82.032 -82.003 -82.412 -82.611 -82.932 -83.301 -83.387 -83.755 -83.598
## 3 -87.085 -87.311 -87.447 -87.487 -87.801 -87.793 -88.179 -88.526 -88.714
## 4 -86.040 -86.201 -86.772 -86.996 -87.480 -88.046 -88.318 -88.672 -88.791
## 5 -97.116 -97.520 -97.603 -98.002 -98.434 -98.590 -98.893 -99.189 -99.455
## 6 -85.559 -85.967 -86.472 -86.579 -87.178 -87.485 -87.809 -88.308 -88.428
## T302 T304 T306 T308 T310 T312 T314 T316
## 1 -86.045 -86.379 -86.819 -87.009 -87.243 -87.362 -87.616 -87.946
## 2 -83.973 -84.307 -84.405 -84.524 -84.784 -85.054 -85.134 -85.136
## 3 -88.847 -89.039 -89.312 -89.584 -89.593 -89.876 -89.839 -90.507
## 4 -89.367 -89.635 -90.028 -90.664 -91.122 -91.308 -91.564 -91.952
## 5 -99.609 -99.937 -100.100 -100.320 -100.470 -100.700 -100.750 -101.080
## 6 -89.134 -89.392 -89.840 -89.814 -90.356 -90.458 -90.625 -90.910
## T318 T320 T322 T324 T326 T328 T330 T332
## 1 -87.977 -88.284 -88.484 -88.772 -89.051 -89.202 -89.261 -89.525
## 2 -85.439 -85.563 -85.709 -85.879 -85.992 -86.095 -86.149 -86.448
## 3 -90.597 -90.877 -91.031 -91.109 -91.273 -91.450 -91.839 -91.979
## 4 -92.240 -92.625 -93.130 -93.292 -93.777 -94.309 -94.519 -94.846
## 5 -101.160 -101.520 -101.570 -101.770 -102.010 -102.170 -102.420 -102.390
## 6 -91.173 -91.513 -91.957 -92.334 -92.822 -92.643 -93.127 -93.484
## T334 T336 T338 T340 T342 T344 T346 T348
## 1 -89.723 -89.862 -90.026 -90.355 -90.443 -90.687 -90.730 -90.978
## 2 -86.536 -86.451 -86.695 -86.751 -86.783 -87.060 -87.229 -87.124
## 3 -91.940 -92.289 -92.713 -92.803 -92.932 -93.384 -93.329 -93.556
## 4 -95.025 -95.227 -95.798 -96.112 -96.386 -96.618 -96.919 -97.099
## 5 -102.840 -102.870 -102.870 -103.090 -103.260 -103.430 -103.570 -103.750
## 6 -93.702 -94.205 -94.332 -94.394 -94.628 -94.787 -94.946 -95.193
## T350 T352 T354 T356 T358 T360 T362 T364
## 1 -91.206 -91.419 -91.394 -91.735 -91.794 -92.140 -92.236 -92.308
## 2 -87.348 -87.360 -87.539 -87.628 -87.816 -87.621 -87.898 -87.725
## 3 -93.822 -94.122 -94.282 -94.682 -94.701 -94.720 -95.087 -95.412
## 4 -97.614 -97.838 -98.052 -98.174 -98.631 -98.987 -99.055 -99.343
## 5 -103.960 -103.990 -104.030 -104.300 -104.270 -104.310 -104.520 -104.590
## 6 -95.493 -95.652 -96.038 -95.958 -96.167 -96.210 -96.371 -96.819
## T366 T368 T370 T372 T374 T376 T378 T380
## 1 -92.396 -92.472 -92.614 -92.715 -92.981 -93.106 -93.136 -93.330
## 2 -87.956 -88.114 -88.235 -88.117 -88.404 -88.450 -88.395 -88.550
## 3 -95.599 -95.679 -96.030 -96.170 -96.307 -96.638 -96.728 -96.900
## 4 -99.689 -99.857 -100.020 -100.370 -100.440 -100.620 -101.000 -101.140
## 5 -104.860 -104.970 -105.110 -105.160 -105.220 -105.510 -105.380 -105.600
## 6 -96.846 -96.926 -97.020 -97.233 -97.629 -97.811 -97.596 -97.767
## T382 T384 T386 T388 T390 T392 T394 T396
## 1 -93.535 -93.622 -93.467 -93.637 -93.701 -94.044 -93.921 -94.127
## 2 -88.894 -88.800 -88.888 -88.917 -89.206 -89.119 -89.149 -89.436
## 3 -97.169 -97.462 -97.303 -97.457 -97.607 -97.979 -97.960 -98.244
## 4 -101.340 -101.440 -101.960 -101.830 -102.320 -102.420 -102.220 -102.570
## 5 -105.670 -105.640 -105.770 -105.940 -105.970 -105.950 -106.110 -106.360
## 6 -98.186 -98.230 -98.299 -98.318 -98.578 -98.589 -98.901 -98.785
## T398 T400 T402 T404 T406 T408 T410 T412
## 1 -94.271 -94.311 -94.284 -94.401 -94.329 -94.258 -94.405 -94.586
## 2 -89.402 -89.636 -89.544 -89.537 -89.522 -89.620 -90.011 -89.965
## 3 -98.601 -98.688 -99.255 -99.294 -99.414 -99.634 -99.706 -100.050
## 4 -102.830 -102.800 -103.140 -103.250 -103.650 -103.670 -103.580 -104.040
## 5 -106.200 -106.400 -106.500 -106.510 -106.700 -106.610 -106.690 -106.770
## 6 -99.164 -99.236 -99.338 -99.498 -99.631 -99.851 -99.913 -99.750
## T414 T416 T418 T420 T422 T424 T426 T428
## 1 -94.574 -94.559 -94.931 -95.030 -95.100 -95.002 -94.916 -94.937
## 2 -89.897 -89.821 -89.972 -90.071 -90.138 -90.228 -90.139 -90.353
## 3 -100.400 -100.280 -100.450 -100.500 -100.720 -100.850 -101.270 -101.170
## 4 -103.890 -103.770 -104.010 -104.140 -103.970 -104.240 -104.180 -104.350
## 5 -106.650 -106.960 -106.920 -107.010 -107.120 -107.200 -107.300 -107.270
## 6 -100.110 -100.060 -100.210 -100.300 -100.720 -100.520 -100.810 -100.310
## T430 T432 T434 T436 T438 T440 T442 T444
## 1 -94.944 -95.006 -95.079 -95.109 -95.011 -95.119 -95.075 -95.137
## 2 -90.601 -90.554 -90.881 -90.826 -90.654 -90.717 -90.799 -90.930
## 3 -101.270 -101.490 -101.500 -101.730 -101.720 -101.890 -102.020 -102.020
## 4 -104.410 -104.600 -104.530 -104.690 -104.640 -104.700 -105.020 -104.690
## 5 -107.220 -107.450 -107.550 -107.580 -107.400 -107.470 -107.580 -107.590
## 6 -100.770 -100.690 -100.940 -100.570 -100.700 -101.000 -100.980 -101.180
## T446 T448 T450 T452 T454 T456 T458 T460
## 1 -95.205 -95.238 -95.257 -95.304 -95.402 -95.519 -95.376 -95.449
## 2 -90.842 -91.178 -91.287 -91.265 -91.464 -91.657 -91.600 -91.697
## 3 -102.120 -102.290 -102.360 -102.590 -102.850 -102.940 -102.800 -102.840
## 4 -104.720 -104.770 -104.810 -104.640 -104.650 -104.940 -104.800 -104.610
## 5 -107.650 -107.720 -107.670 -107.580 -107.600 -107.720 -107.680 -107.790
## 6 -101.120 -101.250 -101.330 -101.310 -101.360 -101.220 -101.390 -101.370
## T462 T464 T466 T468 T470 T472 T474 T476
## 1 -95.503 -95.568 -95.570 -95.554 -95.740 -95.859 -95.846 -95.810
## 2 -91.697 -92.019 -91.974 -92.120 -92.209 -92.246 -92.432 -92.452
## 3 -103.060 -103.270 -103.300 -103.510 -103.600 -103.810 -103.930 -103.720
## 4 -104.600 -104.550 -104.530 -104.530 -104.360 -104.440 -104.290 -104.410
## 5 -107.770 -107.890 -108.020 -108.170 -108.340 -108.310 -108.420 -108.490
## 6 -101.360 -101.380 -101.460 -101.580 -101.650 -101.660 -101.850 -101.970
## T478 T480
## 1 -95.985 -95.967
## 2 -92.303 -92.495
## 3 -103.860 -104.000
## 4 -104.280 -104.210
## 5 -108.380 -108.450
## 6 -102.070 -102.130iplotCorr(grav$pheno)iplotCurves
iplotCurves creates a plot of a set of curves linked to one or two scatterplots.
iplotCurves(grav$pheno)times1 <- as.numeric(sub("T", "", phenames(grav)))/60
times1## [1] 0.00000000 0.03333333 0.06666667 0.10000000 0.13333333 0.16666667
## [7] 0.20000000 0.23333333 0.26666667 0.30000000 0.33333333 0.36666667
## [13] 0.40000000 0.43333333 0.46666667 0.50000000 0.53333333 0.56666667
## [19] 0.60000000 0.63333333 0.66666667 0.70000000 0.73333333 0.76666667
## [25] 0.80000000 0.83333333 0.86666667 0.90000000 0.93333333 0.96666667
## [31] 1.00000000 1.03333333 1.06666667 1.10000000 1.13333333 1.16666667
## [37] 1.20000000 1.23333333 1.26666667 1.30000000 1.33333333 1.36666667
## [43] 1.40000000 1.43333333 1.46666667 1.50000000 1.53333333 1.56666667
## [49] 1.60000000 1.63333333 1.66666667 1.70000000 1.73333333 1.76666667
## [55] 1.80000000 1.83333333 1.86666667 1.90000000 1.93333333 1.96666667
## [61] 2.00000000 2.03333333 2.06666667 2.10000000 2.13333333 2.16666667
## [67] 2.20000000 2.23333333 2.26666667 2.30000000 2.33333333 2.36666667
## [73] 2.40000000 2.43333333 2.46666667 2.50000000 2.53333333 2.56666667
## [79] 2.60000000 2.63333333 2.66666667 2.70000000 2.73333333 2.76666667
## [85] 2.80000000 2.83333333 2.86666667 2.90000000 2.93333333 2.96666667
## [91] 3.00000000 3.03333333 3.06666667 3.10000000 3.13333333 3.16666667
## [97] 3.20000000 3.23333333 3.26666667 3.30000000 3.33333333 3.36666667
## [103] 3.40000000 3.43333333 3.46666667 3.50000000 3.53333333 3.56666667
## [109] 3.60000000 3.63333333 3.66666667 3.70000000 3.73333333 3.76666667
## [115] 3.80000000 3.83333333 3.86666667 3.90000000 3.93333333 3.96666667
## [121] 4.00000000 4.03333333 4.06666667 4.10000000 4.13333333 4.16666667
## [127] 4.20000000 4.23333333 4.26666667 4.30000000 4.33333333 4.36666667
## [133] 4.40000000 4.43333333 4.46666667 4.50000000 4.53333333 4.56666667
## [139] 4.60000000 4.63333333 4.66666667 4.70000000 4.73333333 4.76666667
## [145] 4.80000000 4.83333333 4.86666667 4.90000000 4.93333333 4.96666667
## [151] 5.00000000 5.03333333 5.06666667 5.10000000 5.13333333 5.16666667
## [157] 5.20000000 5.23333333 5.26666667 5.30000000 5.33333333 5.36666667
## [163] 5.40000000 5.43333333 5.46666667 5.50000000 5.53333333 5.56666667
## [169] 5.60000000 5.63333333 5.66666667 5.70000000 5.73333333 5.76666667
## [175] 5.80000000 5.83333333 5.86666667 5.90000000 5.93333333 5.96666667
## [181] 6.00000000 6.03333333 6.06666667 6.10000000 6.13333333 6.16666667
## [187] 6.20000000 6.23333333 6.26666667 6.30000000 6.33333333 6.36666667
## [193] 6.40000000 6.43333333 6.46666667 6.50000000 6.53333333 6.56666667
## [199] 6.60000000 6.63333333 6.66666667 6.70000000 6.73333333 6.76666667
## [205] 6.80000000 6.83333333 6.86666667 6.90000000 6.93333333 6.96666667
## [211] 7.00000000 7.03333333 7.06666667 7.10000000 7.13333333 7.16666667
## [217] 7.20000000 7.23333333 7.26666667 7.30000000 7.33333333 7.36666667
## [223] 7.40000000 7.43333333 7.46666667 7.50000000 7.53333333 7.56666667
## [229] 7.60000000 7.63333333 7.66666667 7.70000000 7.73333333 7.76666667
## [235] 7.80000000 7.83333333 7.86666667 7.90000000 7.93333333 7.96666667
## [241] 8.00000000times <- attr(grav, "time")
times## [1] 0.00000000 0.03333333 0.06666667 0.10000000 0.13333333 0.16666667
## [7] 0.20000000 0.23333333 0.26666667 0.30000000 0.33333333 0.36666667
## [13] 0.40000000 0.43333333 0.46666667 0.50000000 0.53333333 0.56666667
## [19] 0.60000000 0.63333333 0.66666667 0.70000000 0.73333333 0.76666667
## [25] 0.80000000 0.83333333 0.86666667 0.90000000 0.93333333 0.96666667
## [31] 1.00000000 1.03333333 1.06666667 1.10000000 1.13333333 1.16666667
## [37] 1.20000000 1.23333333 1.26666667 1.30000000 1.33333333 1.36666667
## [43] 1.40000000 1.43333333 1.46666667 1.50000000 1.53333333 1.56666667
## [49] 1.60000000 1.63333333 1.66666667 1.70000000 1.73333333 1.76666667
## [55] 1.80000000 1.83333333 1.86666667 1.90000000 1.93333333 1.96666667
## [61] 2.00000000 2.03333333 2.06666667 2.10000000 2.13333333 2.16666667
## [67] 2.20000000 2.23333333 2.26666667 2.30000000 2.33333333 2.36666667
## [73] 2.40000000 2.43333333 2.46666667 2.50000000 2.53333333 2.56666667
## [79] 2.60000000 2.63333333 2.66666667 2.70000000 2.73333333 2.76666667
## [85] 2.80000000 2.83333333 2.86666667 2.90000000 2.93333333 2.96666667
## [91] 3.00000000 3.03333333 3.06666667 3.10000000 3.13333333 3.16666667
## [97] 3.20000000 3.23333333 3.26666667 3.30000000 3.33333333 3.36666667
## [103] 3.40000000 3.43333333 3.46666667 3.50000000 3.53333333 3.56666667
## [109] 3.60000000 3.63333333 3.66666667 3.70000000 3.73333333 3.76666667
## [115] 3.80000000 3.83333333 3.86666667 3.90000000 3.93333333 3.96666667
## [121] 4.00000000 4.03333333 4.06666667 4.10000000 4.13333333 4.16666667
## [127] 4.20000000 4.23333333 4.26666667 4.30000000 4.33333333 4.36666667
## [133] 4.40000000 4.43333333 4.46666667 4.50000000 4.53333333 4.56666667
## [139] 4.60000000 4.63333333 4.66666667 4.70000000 4.73333333 4.76666667
## [145] 4.80000000 4.83333333 4.86666667 4.90000000 4.93333333 4.96666667
## [151] 5.00000000 5.03333333 5.06666667 5.10000000 5.13333333 5.16666667
## [157] 5.20000000 5.23333333 5.26666667 5.30000000 5.33333333 5.36666667
## [163] 5.40000000 5.43333333 5.46666667 5.50000000 5.53333333 5.56666667
## [169] 5.60000000 5.63333333 5.66666667 5.70000000 5.73333333 5.76666667
## [175] 5.80000000 5.83333333 5.86666667 5.90000000 5.93333333 5.96666667
## [181] 6.00000000 6.03333333 6.06666667 6.10000000 6.13333333 6.16666667
## [187] 6.20000000 6.23333333 6.26666667 6.30000000 6.33333333 6.36666667
## [193] 6.40000000 6.43333333 6.46666667 6.50000000 6.53333333 6.56666667
## [199] 6.60000000 6.63333333 6.66666667 6.70000000 6.73333333 6.76666667
## [205] 6.80000000 6.83333333 6.86666667 6.90000000 6.93333333 6.96666667
## [211] 7.00000000 7.03333333 7.06666667 7.10000000 7.13333333 7.16666667
## [217] 7.20000000 7.23333333 7.26666667 7.30000000 7.33333333 7.36666667
## [223] 7.40000000 7.43333333 7.46666667 7.50000000 7.53333333 7.56666667
## [229] 7.60000000 7.63333333 7.66666667 7.70000000 7.73333333 7.76666667
## [235] 7.80000000 7.83333333 7.86666667 7.90000000 7.93333333 7.96666667
## [241] 8.00000000#?attr
phe <- grav$pheno
head(phe)## T0 T2 T4 T6 T8 T10 T12 T14
## 1 -3.4717 -3.2973 -3.4837 -3.8324 -3.9971 -4.4138 -4.6477 -4.9400
## 2 -3.9243 -4.1068 -4.0559 -4.1955 -4.2935 -4.5356 -4.9223 -5.2457
## 3 -7.4022 -7.4470 -7.6809 -7.7515 -8.2363 -8.7546 -9.1621 -9.9663
## 4 -11.0550 -11.0470 -11.1190 -10.8650 -10.9140 -11.2740 -11.2450 -11.2770
## 5 -5.0697 -5.1864 -5.0430 -5.4597 -5.8597 -6.3028 -7.1255 -7.4619
## 6 5.1926 5.3676 5.1018 4.8896 3.9455 3.4340 2.5305 1.8841
## T16 T18 T20 T22 T24 T26 T28 T30
## 1 -5.4629 -5.89310 -6.5397 -6.9486 -7.7236 -8.3297 -8.9201 -9.6447
## 2 -5.5971 -5.89990 -6.1434 -6.6733 -6.9014 -7.5466 -8.0077 -8.3959
## 3 -10.2640 -10.84700 -11.8350 -12.3950 -13.1180 -13.7720 -14.6950 -15.5160
## 4 -11.2500 -11.58300 -11.6780 -11.7850 -12.1060 -12.3240 -12.7620 -13.1980
## 5 -8.3286 -8.80330 -9.7265 -10.7910 -11.7790 -12.3320 -13.4920 -14.5850
## 6 1.1394 0.12565 -0.7945 -1.5589 -2.3704 -3.5434 -4.5057 -5.5834
## T32 T34 T36 T38 T40 T42 T44 T46 T48
## 1 -10.5440 -11.3190 -12.2730 -12.8690 -13.684 -14.672 -15.574 -16.025 -17.142
## 2 -8.7834 -9.3773 -9.8818 -10.5020 -11.141 -11.801 -12.305 -12.818 -13.592
## 3 -16.4600 -17.3470 -17.9860 -19.0310 -20.076 -20.788 -21.927 -22.833 -23.803
## 4 -13.1540 -13.4740 -14.0890 -14.4260 -14.644 -14.873 -15.340 -15.668 -15.841
## 5 -15.6780 -16.7270 -17.9550 -19.1010 -20.226 -21.375 -22.675 -23.858 -25.091
## 6 -6.6111 -7.7473 -8.7888 -9.7522 -10.943 -12.453 -13.494 -14.338 -15.433
## T50 T52 T54 T56 T58 T60 T62 T64 T66
## 1 -17.971 -18.896 -19.540 -20.474 -21.435 -22.227 -23.179 -23.962 -24.929
## 2 -14.161 -15.022 -15.342 -16.059 -16.675 -17.457 -18.453 -19.111 -19.915
## 3 -24.647 -25.486 -26.355 -27.266 -28.387 -29.124 -29.933 -30.812 -31.520
## 4 -16.482 -16.764 -17.297 -17.689 -17.731 -18.308 -18.985 -19.643 -20.051
## 5 -26.122 -27.322 -28.556 -29.358 -30.675 -31.585 -32.698 -33.580 -34.640
## 6 -16.654 -17.801 -18.894 -19.951 -21.298 -22.422 -23.611 -24.827 -26.050
## T68 T70 T72 T74 T76 T78 T80 T82 T84
## 1 -25.849 -26.512 -27.360 -28.071 -28.876 -29.480 -30.484 -31.342 -32.066
## 2 -20.607 -21.012 -21.775 -22.677 -23.546 -24.047 -25.123 -25.930 -26.861
## 3 -32.607 -33.320 -34.355 -35.021 -35.973 -36.892 -37.663 -38.233 -39.279
## 4 -20.590 -20.826 -21.377 -22.112 -22.785 -23.291 -23.720 -24.493 -24.903
## 5 -35.493 -36.559 -37.400 -38.325 -39.268 -40.199 -40.940 -42.132 -43.133
## 6 -27.223 -28.538 -29.651 -30.447 -31.601 -32.785 -34.192 -34.898 -36.201
## T86 T88 T90 T92 T94 T96 T98 T100 T102
## 1 -32.975 -33.658 -34.361 -35.217 -35.801 -36.682 -37.191 -37.905 -38.623
## 2 -27.574 -28.510 -29.406 -30.092 -30.902 -31.909 -32.502 -33.525 -33.981
## 3 -40.028 -40.864 -41.413 -42.298 -42.773 -43.850 -44.697 -45.225 -45.846
## 4 -25.590 -26.123 -26.627 -27.198 -27.916 -28.560 -29.095 -29.969 -30.284
## 5 -44.022 -44.890 -45.764 -47.025 -47.880 -48.632 -49.481 -50.147 -51.120
## 6 -37.196 -38.119 -39.241 -40.157 -40.958 -41.737 -42.447 -43.291 -44.126
## T104 T106 T108 T110 T112 T114 T116 T118 T120
## 1 -39.276 -39.972 -40.838 -41.169 -41.709 -42.570 -43.236 -43.844 -44.508
## 2 -34.874 -35.465 -36.392 -37.208 -37.845 -38.638 -39.458 -40.161 -40.790
## 3 -46.420 -47.342 -47.946 -48.515 -49.104 -49.841 -50.631 -51.493 -51.674
## 4 -30.875 -31.379 -31.936 -32.860 -33.621 -34.162 -34.754 -35.579 -36.356
## 5 -52.096 -52.939 -53.586 -54.542 -55.245 -56.242 -56.697 -57.401 -58.114
## 6 -44.981 -45.666 -46.292 -46.933 -47.495 -48.041 -48.875 -49.195 -49.590
## T122 T124 T126 T128 T130 T132 T134 T136 T138
## 1 -45.052 -45.742 -46.536 -47.143 -47.731 -48.357 -48.989 -49.582 -50.113
## 2 -41.386 -41.937 -42.846 -43.601 -44.303 -44.793 -45.647 -46.480 -46.976
## 3 -52.500 -53.008 -53.776 -54.210 -55.042 -55.609 -56.057 -56.817 -57.477
## 4 -36.638 -37.451 -38.116 -38.886 -39.710 -40.586 -40.961 -41.802 -42.623
## 5 -58.944 -59.784 -60.385 -60.950 -61.756 -62.497 -63.080 -63.700 -64.356
## 6 -50.020 -51.037 -51.472 -51.771 -52.637 -53.307 -53.614 -54.106 -55.052
## T140 T142 T144 T146 T148 T150 T152 T154 T156
## 1 -50.738 -51.421 -52.148 -52.767 -53.312 -53.971 -54.504 -54.942 -55.626
## 2 -47.754 -48.466 -49.081 -49.700 -50.411 -51.143 -51.633 -52.219 -52.914
## 3 -58.019 -58.512 -59.319 -60.087 -60.592 -61.258 -62.023 -62.501 -63.018
## 4 -43.263 -44.073 -44.705 -45.405 -46.227 -46.777 -47.665 -48.273 -48.942
## 5 -65.014 -65.455 -66.229 -66.747 -67.478 -67.972 -68.777 -69.356 -69.908
## 6 -55.645 -56.311 -56.876 -57.440 -57.880 -58.532 -59.336 -59.592 -59.760
## T158 T160 T162 T164 T166 T168 T170 T172 T174
## 1 -56.166 -56.842 -57.229 -57.744 -58.272 -58.910 -59.321 -59.720 -60.315
## 2 -53.578 -54.335 -54.647 -55.308 -56.069 -56.775 -57.100 -57.883 -58.678
## 3 -63.808 -64.185 -64.809 -65.401 -65.899 -66.322 -66.832 -67.566 -68.177
## 4 -49.860 -50.554 -51.010 -52.018 -52.724 -53.145 -53.799 -54.451 -55.146
## 5 -70.409 -71.122 -71.708 -72.204 -72.922 -73.402 -73.978 -74.665 -75.099
## 6 -60.479 -61.198 -61.477 -62.208 -62.526 -63.198 -63.795 -64.299 -64.718
## T176 T178 T180 T182 T184 T186 T188 T190 T192
## 1 -60.667 -61.412 -61.784 -62.301 -62.698 -63.181 -63.630 -64.243 -64.534
## 2 -59.000 -59.640 -60.296 -60.896 -61.238 -61.848 -62.529 -62.856 -63.434
## 3 -68.624 -69.035 -69.486 -69.937 -70.426 -70.882 -71.364 -71.729 -72.124
## 4 -56.160 -56.701 -57.650 -58.093 -58.820 -59.786 -59.997 -60.955 -61.331
## 5 -75.632 -76.167 -76.740 -77.351 -77.759 -78.294 -78.932 -79.457 -79.998
## 6 -65.145 -65.779 -66.081 -66.778 -67.294 -67.950 -68.451 -68.720 -68.932
## T194 T196 T198 T200 T202 T204 T206 T208 T210
## 1 -64.890 -65.510 -66.168 -66.566 -66.998 -67.456 -67.958 -68.519 -69.025
## 2 -63.972 -64.391 -64.868 -65.522 -65.781 -66.487 -66.938 -67.413 -67.899
## 3 -72.640 -73.153 -73.494 -73.927 -74.286 -74.804 -75.422 -75.865 -75.938
## 4 -62.151 -62.819 -63.376 -63.922 -64.867 -65.260 -65.847 -66.765 -67.227
## 5 -80.397 -80.876 -81.357 -81.888 -82.368 -82.940 -83.207 -83.678 -84.140
## 6 -69.186 -69.873 -70.079 -70.873 -71.243 -71.757 -72.046 -72.763 -73.357
## T212 T214 T216 T218 T220 T222 T224 T226 T228
## 1 -69.525 -69.888 -70.340 -70.804 -71.071 -71.628 -71.982 -72.534 -72.884
## 2 -68.506 -68.917 -69.466 -69.803 -70.341 -70.645 -71.224 -71.558 -72.071
## 3 -76.868 -77.163 -77.641 -78.014 -78.117 -78.610 -78.825 -79.463 -79.601
## 4 -67.731 -68.571 -69.307 -69.675 -70.307 -70.896 -71.366 -72.210 -72.740
## 5 -84.789 -85.156 -85.362 -85.674 -86.207 -86.811 -87.143 -87.494 -87.886
## 6 -74.028 -74.683 -75.128 -75.417 -75.609 -76.169 -76.466 -76.502 -76.959
## T230 T232 T234 T236 T238 T240 T242 T244 T246
## 1 -73.329 -73.717 -74.223 -74.507 -74.996 -75.494 -75.904 -76.131 -76.718
## 2 -72.568 -73.052 -73.485 -74.223 -74.447 -74.597 -75.212 -75.660 -76.156
## 3 -79.812 -80.096 -80.464 -80.789 -80.941 -81.391 -81.544 -81.752 -82.099
## 4 -73.236 -73.972 -74.239 -74.816 -75.458 -76.175 -76.523 -76.996 -77.571
## 5 -88.418 -88.701 -89.255 -89.735 -90.126 -90.510 -90.915 -91.455 -91.664
## 6 -77.377 -77.588 -78.068 -77.785 -78.312 -78.332 -78.877 -79.312 -79.207
## T248 T250 T252 T254 T256 T258 T260 T262 T264
## 1 -76.914 -77.289 -77.691 -77.903 -78.405 -78.835 -79.148 -79.571 -79.897
## 2 -76.597 -76.938 -77.248 -77.659 -77.926 -78.193 -78.633 -79.153 -79.166
## 3 -82.477 -82.717 -82.892 -83.298 -83.495 -83.671 -83.969 -84.261 -84.679
## 4 -77.896 -78.290 -78.925 -79.255 -79.696 -80.135 -80.729 -81.152 -81.865
## 5 -91.969 -92.461 -92.869 -93.271 -93.527 -93.960 -94.234 -94.495 -94.789
## 6 -79.655 -79.957 -80.278 -81.010 -81.135 -81.441 -81.601 -81.921 -82.378
## T266 T268 T270 T272 T274 T276 T278 T280 T282
## 1 -80.146 -80.465 -80.836 -81.189 -81.380 -81.813 -82.299 -82.509 -82.760
## 2 -79.639 -79.781 -80.089 -80.429 -80.590 -80.940 -81.026 -81.324 -81.550
## 3 -84.798 -85.107 -85.661 -85.590 -85.700 -86.107 -86.165 -86.559 -86.784
## 4 -82.150 -82.662 -82.905 -83.314 -83.757 -84.402 -84.452 -85.065 -85.416
## 5 -94.885 -95.173 -95.431 -95.605 -95.727 -95.988 -96.357 -96.489 -96.921
## 6 -82.489 -83.009 -83.161 -83.831 -84.098 -84.380 -84.777 -84.856 -85.414
## T284 T286 T288 T290 T292 T294 T296 T298 T300
## 1 -83.180 -83.445 -83.913 -84.118 -84.649 -85.063 -85.222 -85.364 -85.635
## 2 -82.032 -82.003 -82.412 -82.611 -82.932 -83.301 -83.387 -83.755 -83.598
## 3 -87.085 -87.311 -87.447 -87.487 -87.801 -87.793 -88.179 -88.526 -88.714
## 4 -86.040 -86.201 -86.772 -86.996 -87.480 -88.046 -88.318 -88.672 -88.791
## 5 -97.116 -97.520 -97.603 -98.002 -98.434 -98.590 -98.893 -99.189 -99.455
## 6 -85.559 -85.967 -86.472 -86.579 -87.178 -87.485 -87.809 -88.308 -88.428
## T302 T304 T306 T308 T310 T312 T314 T316
## 1 -86.045 -86.379 -86.819 -87.009 -87.243 -87.362 -87.616 -87.946
## 2 -83.973 -84.307 -84.405 -84.524 -84.784 -85.054 -85.134 -85.136
## 3 -88.847 -89.039 -89.312 -89.584 -89.593 -89.876 -89.839 -90.507
## 4 -89.367 -89.635 -90.028 -90.664 -91.122 -91.308 -91.564 -91.952
## 5 -99.609 -99.937 -100.100 -100.320 -100.470 -100.700 -100.750 -101.080
## 6 -89.134 -89.392 -89.840 -89.814 -90.356 -90.458 -90.625 -90.910
## T318 T320 T322 T324 T326 T328 T330 T332
## 1 -87.977 -88.284 -88.484 -88.772 -89.051 -89.202 -89.261 -89.525
## 2 -85.439 -85.563 -85.709 -85.879 -85.992 -86.095 -86.149 -86.448
## 3 -90.597 -90.877 -91.031 -91.109 -91.273 -91.450 -91.839 -91.979
## 4 -92.240 -92.625 -93.130 -93.292 -93.777 -94.309 -94.519 -94.846
## 5 -101.160 -101.520 -101.570 -101.770 -102.010 -102.170 -102.420 -102.390
## 6 -91.173 -91.513 -91.957 -92.334 -92.822 -92.643 -93.127 -93.484
## T334 T336 T338 T340 T342 T344 T346 T348
## 1 -89.723 -89.862 -90.026 -90.355 -90.443 -90.687 -90.730 -90.978
## 2 -86.536 -86.451 -86.695 -86.751 -86.783 -87.060 -87.229 -87.124
## 3 -91.940 -92.289 -92.713 -92.803 -92.932 -93.384 -93.329 -93.556
## 4 -95.025 -95.227 -95.798 -96.112 -96.386 -96.618 -96.919 -97.099
## 5 -102.840 -102.870 -102.870 -103.090 -103.260 -103.430 -103.570 -103.750
## 6 -93.702 -94.205 -94.332 -94.394 -94.628 -94.787 -94.946 -95.193
## T350 T352 T354 T356 T358 T360 T362 T364
## 1 -91.206 -91.419 -91.394 -91.735 -91.794 -92.140 -92.236 -92.308
## 2 -87.348 -87.360 -87.539 -87.628 -87.816 -87.621 -87.898 -87.725
## 3 -93.822 -94.122 -94.282 -94.682 -94.701 -94.720 -95.087 -95.412
## 4 -97.614 -97.838 -98.052 -98.174 -98.631 -98.987 -99.055 -99.343
## 5 -103.960 -103.990 -104.030 -104.300 -104.270 -104.310 -104.520 -104.590
## 6 -95.493 -95.652 -96.038 -95.958 -96.167 -96.210 -96.371 -96.819
## T366 T368 T370 T372 T374 T376 T378 T380
## 1 -92.396 -92.472 -92.614 -92.715 -92.981 -93.106 -93.136 -93.330
## 2 -87.956 -88.114 -88.235 -88.117 -88.404 -88.450 -88.395 -88.550
## 3 -95.599 -95.679 -96.030 -96.170 -96.307 -96.638 -96.728 -96.900
## 4 -99.689 -99.857 -100.020 -100.370 -100.440 -100.620 -101.000 -101.140
## 5 -104.860 -104.970 -105.110 -105.160 -105.220 -105.510 -105.380 -105.600
## 6 -96.846 -96.926 -97.020 -97.233 -97.629 -97.811 -97.596 -97.767
## T382 T384 T386 T388 T390 T392 T394 T396
## 1 -93.535 -93.622 -93.467 -93.637 -93.701 -94.044 -93.921 -94.127
## 2 -88.894 -88.800 -88.888 -88.917 -89.206 -89.119 -89.149 -89.436
## 3 -97.169 -97.462 -97.303 -97.457 -97.607 -97.979 -97.960 -98.244
## 4 -101.340 -101.440 -101.960 -101.830 -102.320 -102.420 -102.220 -102.570
## 5 -105.670 -105.640 -105.770 -105.940 -105.970 -105.950 -106.110 -106.360
## 6 -98.186 -98.230 -98.299 -98.318 -98.578 -98.589 -98.901 -98.785
## T398 T400 T402 T404 T406 T408 T410 T412
## 1 -94.271 -94.311 -94.284 -94.401 -94.329 -94.258 -94.405 -94.586
## 2 -89.402 -89.636 -89.544 -89.537 -89.522 -89.620 -90.011 -89.965
## 3 -98.601 -98.688 -99.255 -99.294 -99.414 -99.634 -99.706 -100.050
## 4 -102.830 -102.800 -103.140 -103.250 -103.650 -103.670 -103.580 -104.040
## 5 -106.200 -106.400 -106.500 -106.510 -106.700 -106.610 -106.690 -106.770
## 6 -99.164 -99.236 -99.338 -99.498 -99.631 -99.851 -99.913 -99.750
## T414 T416 T418 T420 T422 T424 T426 T428
## 1 -94.574 -94.559 -94.931 -95.030 -95.100 -95.002 -94.916 -94.937
## 2 -89.897 -89.821 -89.972 -90.071 -90.138 -90.228 -90.139 -90.353
## 3 -100.400 -100.280 -100.450 -100.500 -100.720 -100.850 -101.270 -101.170
## 4 -103.890 -103.770 -104.010 -104.140 -103.970 -104.240 -104.180 -104.350
## 5 -106.650 -106.960 -106.920 -107.010 -107.120 -107.200 -107.300 -107.270
## 6 -100.110 -100.060 -100.210 -100.300 -100.720 -100.520 -100.810 -100.310
## T430 T432 T434 T436 T438 T440 T442 T444
## 1 -94.944 -95.006 -95.079 -95.109 -95.011 -95.119 -95.075 -95.137
## 2 -90.601 -90.554 -90.881 -90.826 -90.654 -90.717 -90.799 -90.930
## 3 -101.270 -101.490 -101.500 -101.730 -101.720 -101.890 -102.020 -102.020
## 4 -104.410 -104.600 -104.530 -104.690 -104.640 -104.700 -105.020 -104.690
## 5 -107.220 -107.450 -107.550 -107.580 -107.400 -107.470 -107.580 -107.590
## 6 -100.770 -100.690 -100.940 -100.570 -100.700 -101.000 -100.980 -101.180
## T446 T448 T450 T452 T454 T456 T458 T460
## 1 -95.205 -95.238 -95.257 -95.304 -95.402 -95.519 -95.376 -95.449
## 2 -90.842 -91.178 -91.287 -91.265 -91.464 -91.657 -91.600 -91.697
## 3 -102.120 -102.290 -102.360 -102.590 -102.850 -102.940 -102.800 -102.840
## 4 -104.720 -104.770 -104.810 -104.640 -104.650 -104.940 -104.800 -104.610
## 5 -107.650 -107.720 -107.670 -107.580 -107.600 -107.720 -107.680 -107.790
## 6 -101.120 -101.250 -101.330 -101.310 -101.360 -101.220 -101.390 -101.370
## T462 T464 T466 T468 T470 T472 T474 T476
## 1 -95.503 -95.568 -95.570 -95.554 -95.740 -95.859 -95.846 -95.810
## 2 -91.697 -92.019 -91.974 -92.120 -92.209 -92.246 -92.432 -92.452
## 3 -103.060 -103.270 -103.300 -103.510 -103.600 -103.810 -103.930 -103.720
## 4 -104.600 -104.550 -104.530 -104.530 -104.360 -104.440 -104.290 -104.410
## 5 -107.770 -107.890 -108.020 -108.170 -108.340 -108.310 -108.420 -108.490
## 6 -101.360 -101.380 -101.460 -101.580 -101.650 -101.660 -101.850 -101.970
## T478 T480
## 1 -95.985 -95.967
## 2 -92.303 -92.495
## 3 -103.860 -104.000
## 4 -104.280 -104.210
## 5 -108.380 -108.450
## 6 -102.070 -102.130iplotCurves(phe, times)To change the axis labels, pass curves_xlab and curves_ylab using chartOpts.
iplotCurves(phe, times, chartOpts=list(curves_xlab="Time (hrs)",
curves_ylab="Response"))iplotCurves(phe, times, phe[,c("T30", "T240")], phe[,c("T240", "T480")],
chartOpts=list(curves_xlab="Time", curves_ylab="Root tip angle",
scat1_xlab="Angle at 30 min", scat1_ylab="Angle at 4 hrs",
scat2_xlab="Angle at 4 hrs", scat2_ylab="Angle at 8 hrs"))Let’s further run a single-QTL genome scan with each individual time point.
grav <- calc.genoprob(grav, step=1)
out.em <- scanone(grav, pheno.col=1:nphe(grav))
summary(out.em)## chr pos T0 T2 T4 T6 T8 T10 T12 T14 T16
## c1.loc44 1 44.0 1.780 1.822 1.842 1.712 1.628 1.610 1.464 1.371 1.242
## Erecta 2 42.7 0.813 0.804 0.871 0.843 0.829 0.844 0.847 0.821 0.834
## GB.97L-Col/99C 3 76.1 3.304 3.249 3.270 3.367 3.438 3.503 3.558 3.663 3.776
## g4539 4 40.3 2.578 2.636 2.674 2.768 2.851 3.014 3.130 3.175 3.324
## c5.loc29 5 29.0 1.710 1.644 1.651 1.688 1.693 1.703 1.673 1.696 1.668
## T18 T20 T22 T24 T26 T28 T30 T32 T34 T36 T38
## c1.loc44 1.186 1.13 1.044 0.964 0.908 0.816 0.782 0.666 0.618 0.555 0.507
## Erecta 0.829 0.81 0.801 0.794 0.725 0.701 0.685 0.656 0.624 0.584 0.559
## GB.97L-Col/99C 3.789 3.78 3.813 3.879 3.927 3.939 4.021 4.066 4.067 4.083 4.089
## g4539 3.539 3.65 3.803 3.906 4.175 4.358 4.442 4.594 4.755 4.732 4.852
## c5.loc29 1.671 1.68 1.659 1.673 1.668 1.694 1.673 1.621 1.588 1.612 1.551
## T40 T42 T44 T46 T48 T50 T52 T54 T56 T58
## c1.loc44 0.453 0.377 0.368 0.319 0.299 0.279 0.252 0.211 0.192 0.173
## Erecta 0.530 0.508 0.456 0.412 0.411 0.391 0.359 0.324 0.329 0.285
## GB.97L-Col/99C 4.093 4.021 4.003 3.966 3.925 3.788 3.786 3.650 3.591 3.432
## g4539 4.993 5.078 5.148 5.194 5.205 5.207 5.206 5.191 5.284 5.196
## c5.loc29 1.523 1.478 1.442 1.403 1.351 1.299 1.284 1.234 1.161 1.088
## T60 T62 T64 T66 T68 T70 T72 T74 T76 T78
## c1.loc44 0.162 0.144 0.129 0.130 0.120 0.106 0.113 0.102 0.101 0.0982
## Erecta 0.290 0.255 0.241 0.230 0.217 0.214 0.206 0.175 0.163 0.1508
## GB.97L-Col/99C 3.339 3.194 3.073 2.917 2.786 2.737 2.567 2.479 2.342 2.2106
## g4539 5.157 5.250 5.222 5.133 5.070 5.027 4.992 4.919 4.853 4.8570
## c5.loc29 1.074 1.042 1.018 0.966 0.922 0.874 0.847 0.814 0.773 0.7299
## T80 T82 T84 T86 T88 T90 T92 T94 T96 T98
## c1.loc44 0.103 0.104 0.105 0.098 0.114 0.105 0.1074 0.1172 0.1093 0.1131
## Erecta 0.148 0.132 0.121 0.127 0.119 0.102 0.0914 0.0814 0.0728 0.0601
## GB.97L-Col/99C 2.107 2.005 1.910 1.759 1.686 1.653 1.5581 1.4445 1.3915 1.3201
## g4539 4.708 4.680 4.654 4.628 4.556 4.553 4.4591 4.4817 4.3949 4.3519
## c5.loc29 0.698 0.688 0.652 0.592 0.558 0.566 0.4952 0.5057 0.4541 0.4501
## T100 T102 T104 T106 T108 T110 T112 T114 T116
## c1.loc44 0.113 0.1161 0.1200 0.1162 0.1222 0.1318 0.120 0.1385 0.1412
## Erecta 0.054 0.0475 0.0378 0.0359 0.0314 0.0244 0.020 0.0152 0.0156
## GB.97L-Col/99C 1.261 1.2025 1.1539 1.0763 1.0138 0.9615 0.911 0.8466 0.8023
## g4539 4.293 4.1805 4.1896 4.1700 4.0484 4.0468 4.015 3.9296 3.9327
## c5.loc29 0.437 0.4027 0.3911 0.3671 0.3425 0.3148 0.315 0.2957 0.2773
## T118 T120 T122 T124 T126 T128 T130 T132
## c1.loc44 0.1360 0.152 0.15143 0.15900 0.17063 0.17645 0.17927 0.18166
## Erecta 0.0111 0.012 0.00674 0.00573 0.00458 0.00189 0.00255 0.00135
## GB.97L-Col/99C 0.7444 0.716 0.65531 0.63656 0.60478 0.54165 0.50956 0.49153
## g4539 3.8783 3.836 3.80218 3.76031 3.70400 3.67065 3.65312 3.58698
## c5.loc29 0.2561 0.261 0.23715 0.22998 0.21391 0.21672 0.19669 0.19553
## T134 T136 T138 T140 T142 T144 T146
## c1.loc44 0.18838 0.200526 0.211210 2.23e-01 0.24882 2.53e-01 0.262278
## Erecta 0.00114 0.000444 0.000154 2.97e-05 0.00013 4.98e-05 0.000272
## GB.97L-Col/99C 0.46660 0.441596 0.405226 3.93e-01 0.36492 3.44e-01 0.331472
## g4539 3.56984 3.542344 3.486263 3.40e+00 3.34004 3.31e+00 3.270049
## c5.loc29 0.17890 0.165895 0.162122 1.53e-01 0.14751 1.31e-01 0.125276
## T148 T150 T152 T154 T156 T158 T160
## c1.loc44 0.272682 0.279281 0.298193 0.29524 0.29935 0.31341 0.32872
## Erecta 0.000416 0.000885 0.000729 0.00107 0.00189 0.00134 0.00135
## GB.97L-Col/99C 0.308773 0.295154 0.284065 0.28366 0.25365 0.24321 0.23482
## g4539 3.234039 3.224584 3.128876 3.17559 3.12258 3.12157 3.08016
## c5.loc29 0.118572 0.105380 0.093622 0.09015 0.08540 0.07706 0.07418
## T162 T164 T166 T168 T170 T172 T174
## c1.loc44 0.323343 0.33839 0.33692 0.36917 0.37012 0.37684 0.39158
## Erecta 0.000837 0.00177 0.00115 0.00167 0.00211 0.00181 0.00177
## GB.97L-Col/99C 0.227744 0.20263 0.19547 0.18645 0.17475 0.17255 0.15555
## g4539 3.050831 2.98835 2.97544 3.00035 2.90233 2.91546 2.87291
## c5.loc29 0.059698 0.05675 0.04845 0.04924 0.03698 0.03344 0.03087
## T176 T178 T180 T182 T184 T186 T188
## c1.loc44 0.396246 0.41637 0.42279 0.440910 0.454834 0.493805 0.500053
## Erecta 0.000677 0.00122 0.00151 0.000588 0.000504 0.000524 0.000126
## GB.97L-Col/99C 0.141199 0.13123 0.11933 0.109243 0.105593 0.102506 0.087941
## g4539 2.858374 2.85743 2.81643 2.857460 2.836303 2.810561 2.796151
## c5.loc29 0.028621 0.02706 0.01991 0.017709 0.014348 0.012225 0.009042
## T190 T192 T194 T196 T198 T200 T202
## c1.loc44 5.19e-01 5.36e-01 0.559014 0.571271 0.599888 0.621620 6.46e-01
## Erecta 2.96e-06 1.04e-05 0.000147 0.000186 0.000393 0.000881 7.65e-04
## GB.97L-Col/99C 8.29e-02 8.00e-02 0.080873 0.070472 0.058110 0.064294 5.86e-02
## g4539 2.75e+00 2.74e+00 2.748984 2.665410 2.642587 2.619363 2.61e+00
## c5.loc29 8.24e-03 3.89e-03 0.002621 0.001385 0.000198 0.000098 5.42e-05
## T204 T206 T208 T210 T212 T214 T216 T218
## c1.loc44 0.655144 0.64923 0.66137 0.68471 0.69256 0.69895 0.6954 0.71307
## Erecta 0.001254 0.00127 0.00169 0.00252 0.00271 0.00213 0.0043 0.00378
## GB.97L-Col/99C 0.055329 0.05135 0.04856 0.04642 0.04204 0.03879 0.0409 0.03847
## g4539 2.550633 2.52180 2.48893 2.48554 2.45776 2.42447 2.3822 2.38851
## c5.loc29 0.000917 0.00213 0.00271 0.00592 0.00642 0.00830 0.0111 0.01282
## T220 T222 T224 T226 T228 T230 T232 T234
## c1.loc44 0.72830 0.74445 0.75003 0.78330 0.77869 0.8168 0.8040 0.8103
## Erecta 0.00394 0.00468 0.00463 0.00684 0.00885 0.0111 0.0110 0.0118
## GB.97L-Col/99C 0.03449 0.04053 0.03421 0.03742 0.03981 0.0373 0.0363 0.0360
## g4539 2.37411 2.33230 2.30010 2.29325 2.26351 2.2647 2.2591 2.2438
## c5.loc29 0.01828 0.02038 0.02399 0.02620 0.02934 0.0348 0.0402 0.0434
## T236 T238 T240 T242 T244 T246 T248 T250 T252
## c1.loc44 0.8352 0.8480 0.8561 0.8762 0.8780 0.8872 0.8948 0.9196 0.9183
## Erecta 0.0130 0.0167 0.0167 0.0200 0.0194 0.0228 0.0262 0.0295 0.0308
## GB.97L-Col/99C 0.0304 0.0315 0.0319 0.0366 0.0360 0.0348 0.0358 0.0334 0.0350
## g4539 2.2470 2.1782 2.1799 2.1700 2.1314 2.1362 2.0986 2.0560 2.0489
## c5.loc29 0.0480 0.0533 0.0542 0.0587 0.0609 0.0627 0.0719 0.0804 0.0837
## T254 T256 T258 T260 T262 T264 T266 T268 T270
## c1.loc44 0.9008 0.8941 0.9243 0.9267 0.9199 0.9424 0.9541 0.9548 0.9635
## Erecta 0.0370 0.0397 0.0429 0.0477 0.0468 0.0550 0.0546 0.0609 0.0634
## GB.97L-Col/99C 0.0300 0.0349 0.0309 0.0319 0.0334 0.0322 0.0340 0.0333 0.0333
## g4539 2.0478 2.0616 2.0569 2.0411 2.0029 1.9944 1.9557 1.9614 1.9281
## c5.loc29 0.0948 0.1004 0.0952 0.1137 0.1279 0.1301 0.1452 0.1434 0.1552
## T272 T274 T276 T278 T280 T282 T284 T286 T288
## c1.loc44 0.9628 0.9545 0.9626 0.9998 0.9745 1.003 0.9986 1.0100 1.0142
## Erecta 0.0711 0.0789 0.0856 0.0988 0.0921 0.104 0.1083 0.1167 0.1238
## GB.97L-Col/99C 0.0406 0.0405 0.0464 0.0438 0.0529 0.050 0.0542 0.0544 0.0578
## g4539 1.8790 1.9026 1.8656 1.8545 1.8147 1.830 1.8175 1.8066 1.7481
## c5.loc29 0.1653 0.1681 0.1715 0.1818 0.1915 0.201 0.1959 0.2032 0.2208
## T290 T292 T294 T296 T298 T300 T302 T304 T306
## c1.loc44 0.9898 1.0008 1.0239 1.0182 0.9831 1.0024 0.997 1.0115 1.0026
## Erecta 0.1234 0.1249 0.1338 0.1500 0.1433 0.1652 0.170 0.1784 0.1947
## GB.97L-Col/99C 0.0671 0.0545 0.0652 0.0683 0.0705 0.0753 0.073 0.0783 0.0814
## g4539 1.7362 1.7344 1.6823 1.6927 1.6749 1.6671 1.645 1.6013 1.5893
## c5.loc29 0.2181 0.2401 0.2383 0.2418 0.2558 0.2604 0.266 0.2732 0.2872
## T308 T310 T312 T314 T316 T318 T320 T322 T324
## c1.loc44 0.9855 0.9901 0.968 0.9642 0.9800 0.9719 0.9473 0.9573 0.908
## Erecta 0.2023 0.2108 0.213 0.2280 0.2234 0.2291 0.2437 0.2607 0.257
## GB.97L-Col/99C 0.0746 0.0822 0.087 0.0876 0.0842 0.0927 0.0972 0.0965 0.102
## g4539 1.5861 1.5758 1.585 1.5521 1.5318 1.5358 1.5023 1.4730 1.445
## c5.loc29 0.2841 0.2971 0.300 0.3155 0.3146 0.3140 0.3330 0.3370 0.373
## T326 T328 T330 T332 T334 T336 T338 T340 T342 T344
## c1.loc44 0.900 0.919 0.900 0.900 0.867 0.876 0.891 0.901 0.871 0.879
## Erecta 0.274 0.271 0.285 0.278 0.276 0.301 0.293 0.305 0.316 0.315
## GB.97L-Col/99C 0.101 0.108 0.104 0.104 0.108 0.118 0.110 0.101 0.113 0.117
## g4539 1.443 1.426 1.419 1.406 1.368 1.375 1.366 1.351 1.356 1.330
## c5.loc29 0.385 0.372 0.387 0.391 0.396 0.393 0.406 0.401 0.403 0.409
## T346 T348 T350 T352 T354 T356 T358 T360 T362 T364
## c1.loc44 0.879 0.872 0.870 0.842 0.866 0.841 0.819 0.802 0.799 0.778
## Erecta 0.312 0.316 0.319 0.332 0.332 0.357 0.355 0.368 0.366 0.369
## GB.97L-Col/99C 0.113 0.119 0.114 0.115 0.119 0.113 0.116 0.111 0.120 0.119
## g4539 1.324 1.326 1.294 1.319 1.296 1.269 1.264 1.267 1.240 1.207
## c5.loc29 0.412 0.414 0.429 0.423 0.424 0.428 0.447 0.432 0.433 0.440
## T366 T368 T370 T372 T374 T376 T378 T380 T382 T384
## c1.loc44 0.764 0.755 0.749 0.703 0.702 0.695 0.661 0.652 0.668 0.627
## Erecta 0.367 0.390 0.386 0.382 0.403 0.381 0.390 0.391 0.400 0.403
## GB.97L-Col/99C 0.125 0.126 0.125 0.118 0.126 0.128 0.129 0.137 0.133 0.139
## g4539 1.184 1.183 1.161 1.148 1.129 1.109 1.116 1.107 1.111 1.094
## c5.loc29 0.443 0.457 0.471 0.484 0.457 0.465 0.459 0.464 0.470 0.468
## T386 T388 T390 T392 T394 T396 T398 T400 T402 T404
## c1.loc44 0.619 0.581 0.582 0.570 0.528 0.532 0.510 0.479 0.470 0.448
## Erecta 0.385 0.379 0.390 0.379 0.377 0.363 0.375 0.369 0.353 0.342
## GB.97L-Col/99C 0.143 0.141 0.146 0.147 0.168 0.154 0.164 0.186 0.181 0.183
## g4539 1.063 1.064 1.027 1.045 1.039 1.025 1.014 0.997 0.991 0.956
## c5.loc29 0.456 0.461 0.458 0.454 0.459 0.459 0.442 0.424 0.441 0.453
## T406 T408 T410 T412 T414 T416 T418 T420 T422 T424
## c1.loc44 0.442 0.423 0.416 0.413 0.387 0.387 0.366 0.369 0.333 0.331
## Erecta 0.341 0.351 0.337 0.332 0.325 0.319 0.328 0.330 0.333 0.333
## GB.97L-Col/99C 0.195 0.203 0.190 0.195 0.199 0.205 0.203 0.204 0.212 0.216
## g4539 0.937 0.931 0.916 0.920 0.913 0.877 0.858 0.855 0.837 0.845
## c5.loc29 0.429 0.437 0.427 0.429 0.410 0.412 0.404 0.391 0.379 0.376
## T426 T428 T430 T432 T434 T436 T438 T440 T442 T444
## c1.loc44 0.309 0.315 0.288 0.275 0.279 0.273 0.255 0.256 0.260 0.243
## Erecta 0.316 0.304 0.300 0.284 0.300 0.297 0.278 0.283 0.267 0.271
## GB.97L-Col/99C 0.217 0.218 0.217 0.218 0.219 0.207 0.212 0.223 0.207 0.214
## g4539 0.831 0.831 0.801 0.767 0.773 0.770 0.737 0.719 0.689 0.697
## c5.loc29 0.402 0.365 0.369 0.358 0.330 0.339 0.316 0.307 0.310 0.297
## T446 T448 T450 T452 T454 T456 T458 T460 T462 T464
## c1.loc44 0.237 0.229 0.216 0.212 0.197 0.204 0.198 0.200 0.183 0.192
## Erecta 0.265 0.245 0.259 0.260 0.255 0.242 0.239 0.241 0.240 0.241
## GB.97L-Col/99C 0.226 0.223 0.221 0.217 0.224 0.216 0.222 0.220 0.217 0.217
## g4539 0.674 0.661 0.623 0.625 0.599 0.570 0.547 0.547 0.503 0.471
## c5.loc29 0.289 0.289 0.283 0.270 0.256 0.254 0.260 0.247 0.250 0.251
## T466 T468 T470 T472 T474 T476 T478 T480
## c1.loc44 0.190 0.177 0.180 0.171 0.152 0.160 0.154 0.140
## Erecta 0.243 0.230 0.233 0.235 0.212 0.225 0.227 0.218
## GB.97L-Col/99C 0.210 0.216 0.211 0.220 0.203 0.205 0.210 0.214
## g4539 0.455 0.452 0.410 0.417 0.392 0.367 0.353 0.353
## c5.loc29 0.248 0.237 0.244 0.236 0.218 0.229 0.202 0.213Charts within a loop
If you want to produce multiple interactive charts within a loop.
times_m <- c("T30", "T240", "T480")
times_clear <- c("30 mins", "4 hours", "8 hours")
times_number <- match(times_m, phenames(grav))
for(i in 1:length(times_m))
print(iplotScanone(out.em, lodcolumn=times_number[i], chartOpts=list(title=times_clear[i])))That’s it, let’s take a break!
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